Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "60" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 52 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 50 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459867 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.952105 | 16.575467 | 56.086599 | 58.136483 | 5.793077 | 9.988247 | 3.469072 | 3.906823 | 0.0274 | 0.0271 | 0.0015 | nan | nan |
| 2459866 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 17.284217 | 18.841558 | 52.863759 | 54.983755 | 6.609401 | 11.257394 | 3.170844 | 3.409923 | 0.0277 | 0.0285 | 0.0016 | nan | nan |
| 2459865 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 18.913481 | 21.316751 | 64.170074 | 66.966103 | 15.752407 | 24.054602 | 15.486927 | 13.182711 | 0.0268 | 0.0262 | 0.0013 | nan | nan |
| 2459864 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 23.134579 | 25.820697 | 25.675546 | 27.398394 | 8.275634 | 13.447906 | 5.630088 | 7.332884 | 0.0272 | 0.0264 | 0.0015 | nan | nan |
| 2459863 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.932744 | 15.555540 | 8.123695 | 8.839677 | 2.996595 | 5.107489 | 2.579242 | 3.061211 | 0.0269 | 0.0264 | 0.0014 | nan | nan |
| 2459862 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.762795 | 15.744755 | 29.344316 | 31.493152 | 12.517758 | 19.071615 | 1.926910 | 2.322570 | 0.0274 | 0.0279 | 0.0015 | nan | nan |
| 2459861 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.640920 | 11.671165 | 8.200552 | 9.080429 | 2.563785 | 3.266038 | 2.077862 | 2.672264 | 0.0268 | 0.0264 | 0.0014 | nan | nan |
| 2459860 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.676488 | 12.886550 | 24.635765 | 26.306040 | 14.753183 | 21.766337 | 2.492707 | 2.935253 | 0.0266 | 0.0266 | 0.0014 | nan | nan |
| 2459859 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.739351 | 10.937426 | 8.873575 | 9.761530 | 2.256655 | 2.836266 | 1.024709 | 1.520458 | 0.0272 | 0.0268 | 0.0016 | nan | nan |
| 2459858 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 10.550244 | 11.608508 | 9.178806 | 10.007035 | 2.286661 | 2.958161 | 1.934819 | 2.878506 | 0.0266 | 0.0267 | 0.0007 | 1.129492 | 1.124510 |
| 2459857 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 6.107262 | 6.713872 | 1.496493 | 1.614892 | 1.205828 | 2.815743 | 5.880468 | 10.444087 | 0.0261 | 0.0238 | 0.0014 | nan | nan |
| 2459856 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 15.735192 | 17.599416 | 23.176217 | 24.631786 | 6.654030 | 12.240390 | 2.841963 | 4.379236 | 0.0280 | 0.0284 | 0.0010 | 1.185756 | 1.178537 |
| 2459855 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 16.487810 | 17.162335 | 24.203167 | 25.488042 | 2.746189 | 4.338307 | 1.392116 | 1.913045 | 0.0273 | 0.0271 | 0.0008 | 1.187427 | 1.182376 |
| 2459854 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 16.537272 | 15.473482 | 18.546580 | 19.461940 | 3.869312 | 4.317459 | 4.308719 | 5.437847 | 0.0270 | 0.0270 | 0.0008 | 1.138912 | 1.136617 |
| 2459853 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 294.578223 | 294.003975 | inf | inf | 4411.442915 | 4229.465736 | 10804.572349 | 9469.467256 | nan | nan | nan | 0.000000 | 0.000000 |
| 2459852 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 13.526697 | 16.659967 | 25.954312 | 28.118259 | 15.613264 | 19.951122 | 16.783592 | 16.591370 | 0.0271 | 0.0268 | 0.0009 | 1.161849 | 1.154627 |
| 2459851 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 10.815498 | 20.760685 | 26.675855 | 30.151158 | 21.516623 | 43.596072 | 13.298725 | 21.999729 | 0.0276 | 0.0312 | 0.0032 | 1.062239 | 1.053452 |
| 2459850 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 13.136291 | 18.230219 | 23.061879 | 25.096854 | 10.327660 | 19.918281 | 6.597873 | 17.947985 | 0.0265 | 0.0252 | 0.0012 | 1.147297 | 1.139243 |
| 2459849 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 15.260327 | 16.613896 | 46.561346 | 49.620940 | 7.168593 | 13.003060 | 4.855984 | 8.362363 | 0.0267 | 0.0254 | 0.0012 | 1.147237 | 1.142030 |
| 2459848 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 13.679524 | 15.035785 | 29.561586 | 32.088618 | 14.304249 | 21.843181 | 1.724090 | 3.354350 | 0.0266 | 0.0257 | 0.0011 | 1.154293 | 1.149149 |
| 2459847 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 15.771136 | 17.469636 | 27.583753 | 30.279560 | 21.865006 | 28.305564 | 0.424208 | 0.883354 | 0.0264 | 0.0256 | 0.0010 | 1.169570 | 1.167373 |
| 2459846 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 25.348015 | 26.168524 | 33.552259 | 35.272309 | 21.656050 | 21.028721 | 4.061417 | 3.992793 | 0.0267 | 0.0265 | 0.0011 | 1.120180 | 1.119672 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 58.136483 | 14.952105 | 16.575467 | 56.086599 | 58.136483 | 5.793077 | 9.988247 | 3.469072 | 3.906823 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 54.983755 | 18.841558 | 17.284217 | 54.983755 | 52.863759 | 11.257394 | 6.609401 | 3.409923 | 3.170844 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 66.966103 | 18.913481 | 21.316751 | 64.170074 | 66.966103 | 15.752407 | 24.054602 | 15.486927 | 13.182711 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 27.398394 | 25.820697 | 23.134579 | 27.398394 | 25.675546 | 13.447906 | 8.275634 | 7.332884 | 5.630088 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Shape | 15.555540 | 13.932744 | 15.555540 | 8.123695 | 8.839677 | 2.996595 | 5.107489 | 2.579242 | 3.061211 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 31.493152 | 13.762795 | 15.744755 | 29.344316 | 31.493152 | 12.517758 | 19.071615 | 1.926910 | 2.322570 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Shape | 11.671165 | 11.671165 | 10.640920 | 9.080429 | 8.200552 | 3.266038 | 2.563785 | 2.672264 | 2.077862 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 26.306040 | 11.676488 | 12.886550 | 24.635765 | 26.306040 | 14.753183 | 21.766337 | 2.492707 | 2.935253 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Shape | 10.937426 | 9.739351 | 10.937426 | 8.873575 | 9.761530 | 2.256655 | 2.836266 | 1.024709 | 1.520458 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Shape | 11.608508 | 11.608508 | 10.550244 | 10.007035 | 9.178806 | 2.958161 | 2.286661 | 2.878506 | 1.934819 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Temporal Discontinuties | 10.444087 | 6.713872 | 6.107262 | 1.614892 | 1.496493 | 2.815743 | 1.205828 | 10.444087 | 5.880468 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 24.631786 | 15.735192 | 17.599416 | 23.176217 | 24.631786 | 6.654030 | 12.240390 | 2.841963 | 4.379236 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 25.488042 | 17.162335 | 16.487810 | 25.488042 | 24.203167 | 4.338307 | 2.746189 | 1.913045 | 1.392116 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 19.461940 | 15.473482 | 16.537272 | 19.461940 | 18.546580 | 4.317459 | 3.869312 | 5.437847 | 4.308719 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | inf | 294.003975 | 294.578223 | inf | inf | 4229.465736 | 4411.442915 | 9469.467256 | 10804.572349 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 28.118259 | 13.526697 | 16.659967 | 25.954312 | 28.118259 | 15.613264 | 19.951122 | 16.783592 | 16.591370 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Temporal Variability | 43.596072 | 10.815498 | 20.760685 | 26.675855 | 30.151158 | 21.516623 | 43.596072 | 13.298725 | 21.999729 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 25.096854 | 13.136291 | 18.230219 | 23.061879 | 25.096854 | 10.327660 | 19.918281 | 6.597873 | 17.947985 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 49.620940 | 15.260327 | 16.613896 | 46.561346 | 49.620940 | 7.168593 | 13.003060 | 4.855984 | 8.362363 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 32.088618 | 15.035785 | 13.679524 | 32.088618 | 29.561586 | 21.843181 | 14.304249 | 3.354350 | 1.724090 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 30.279560 | 17.469636 | 15.771136 | 30.279560 | 27.583753 | 28.305564 | 21.865006 | 0.883354 | 0.424208 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 60 | N05 | digital_maintenance | nn Power | 35.272309 | 25.348015 | 26.168524 | 33.552259 | 35.272309 | 21.656050 | 21.028721 | 4.061417 | 3.992793 |